TEXT-TO-IMAGE GENERATION USING STABLE DIFFUSION.pptx

TEXT-TO-IMAGE GENERATION USING STABLE DIFFUSION.pptx

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  TEXT-TO-IMAGE GENERATION USING STABLEDIFFUSION Under the supervision of Dr. P Chandra Shaker Reddy sir Professor School of CS &AL NAME HALL TICKET NUMBER P. Harsha Vardhan Reddy 2103A52180 D. Saketh Reddy 2103A52178 E. Sai Krishna Reddy 2103A52014 T. Deekshitha Rao 2103A52179 E. Swapna 2103A52044
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  INTRODUCTION One of themost evocative applications of artificial intelligence is text-to-image generation. It transforms textual descriptions into realistic images concerning the context. Among many approaches to this problem, Stable Diffusion is one because of its efficiency and scalability in producing high-quality images. Built on the principles of diffusion probabilistic models, Stable Diffusion is an innovative generative model that exemplifies the current pinnacle of AI-driven creativity. Stable Diffusion essentially works through a stepwise process of converting noise into coherent data (images) based on a textual input prompt.
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  EXISTING SYSTEM Limitations:  Currenttext-to-image models, such as DALL-E and Imagen, rely on semantic priors but cannot reconstruct specific subjects from reference images.  Such models could output variations of the content of image representations but fail to produce high-fidelity representations of specific subjects in novel contexts.  Language drift: models make associations between the class name and the instance itself too strong, making diversity in generated results weak. Capabilities:  Semantic priors are very strong and allow the model to generate diverse instances of general classes ("dog," "car") by textual description; though.  They can synthesize visually good and coherent images, they are not personalized.
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  PROPOSED SYSTEM Objective: Stable Diffusion (v1-5): A pre-trained text-to-image diffusion model (runwayml/stable-diffusion-v1-5) known for its ability to generate visually appealing images from descriptive textual input.  Pre-trained on a large dataset (e.g., LAION- 5B), the model understands diverse textual prompts and can render corresponding visuals with remarkable detail.
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  PROPOSED SYSTEM Key Innovations  Fine-tunepre-trained text-to-image models by using a unique identifier associated with the prompt.  Utilize class-specific knowledge already encoded in the model to produce diverse and high-fidelity renditions.  Prevents language drift by keeping the total class diversity while preserving the specificity of the topic.  It ensures that the produced images are contextually diverse yet keep the core features of the subject under focus. Applications  Subject Recontextualization: Align specific subjects with completely different contexts (for instance, a dog in space).  Artistic Rendering: Generate artistic renderings of subjects but keeping their respective features intact.  Text-Guided View Synthesis: Generate images of subjects in other views, poses, and lighting conditions.
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  Literature Review Author(s) YearModel Used Key Factors Merits Limitations P.Mahalakshmi 2022 LSTM • Deep learning models • Captioning • Information retrieval • High-Quality Output • Enhanced Understanding • Flexibility • Complexity • Depending on quality of text Limited Sadia Ramzan 2022 GAN • Training data • Evaluation matrices • Architecture • Improved image quality • Fine-Grained Detail • Quantitative & Qualitative Evaluation • Complexity & Intensity • Depends on the Quality of Text. Ming Ding 2023 CogView • Fine tuning • Stability • Performance matrices • State of art performance • Versatile application Innovative model • Complexity & Intensity • Depend on Quality of training
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  Workflow Diagram
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  DESIGN Software Requirements:  OperatingSystem: Windows 11, Linux, or macOS.  Programming Language: Python 3.8 or later.  Integrated Development Environment (IDE): Jupyter Notebook, Google Colab Pro, or VS Code.  Libraries: PyTorch, Transformers, Hugging Face Diffusers, NumPy, Pillow, Matplotlib, Scikit-learn, Gradio (for Interface).
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  DESIGN Hardware Requirements:  Processor(CPU): 2 x 64-bit, 3.0 GHz quad-core CPUs or better.  Graphics Processing Unit (GPU): NVIDIA GPU with CUDA support (e.g., NVIDIA RTX 2060 or better).  Memory (RAM): Minimum 16 GB.  Storage: Minimum 50 GB free (preferably SSD for faster access).  Input Devices: Standard Windows or Mac Keyboard, Mouse.  Display: Full HD Monitor or better.
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  DATASET The Stable Diffusionmodel was trained using LAION-5B, a publicly available large-scale dataset containing image-text pairs. It is one of the largest datasets designed for text-to-image and image-to-text model training. “LAION 5B" is also sometimes referred to as "LAION- 5B“. Large-Scale Artificial Intelligence Open Network 5 Billion dataset", or simply “The LAION dataset essentially signifying a massive, open-source collection of 5.85 billion image-text pairs used for training AI models, particularly in the field of image generation. "LAION-2B-en" is sometimes used to refer to the English subset of LAION 5B which contains around 2.3 billion image-text pairs.
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  DATASET
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  STABLE DIFFUSION Stable Diffusion operateson a stepwise process of converting noise into coherent data (images) based on a textual input prompt. The approach involves: • Diffusion Process: Initially, a noisy latent representation of an image is generated. • Denoising Process: Through iterative steps, noise is gradually reduced, conditioned on the input text prompt. This process employs a pre-trained U-Net architecture for denoising, guided by the prompt embeddings. • Text-Conditioning: The model integrates embeddings from a text encoder, typically based on Transformers like CLIP (Contrastive Language–Image Pre-training), ensuring the generated image aligns with the semantic content of the prompt.
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  STABLE DIFFUSION Latent diffusion isthe research on top of which Stable Diffusion was built. It was proposed in High- Resolution Image Synthesis with Latent Diffusion Models There are three main components in latent diffusion: • Autoencoder • U-Net • Text Encoder
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  AUTOENCODER An autoencoder isdesigned to learn a compressed version of the input image. A Variational Autoencoder consists of two main parts i.e. an encoder and a decoder. The encoder's task is to compress the image into a latent form, and then the decoder uses this latent representation to recreate the original image.
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  U-NET U-Net is aconvolutional neural network (CNN) used to clean up an image's latent representation. It's made up of a series of encoder-decoder blocks that increase the image quality step by step. The encoder part of the network reduces the image to a lower resolution, and then the decoder works to restore this compressed image to its original, higher resolution and eliminate any noise in the process.
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  TEXT ENCODER The jobof the text encoder is to convert text prompts into a latent form. Typically, this is achieved using a transformer-based model, such as the Text Encoder from CLIP, which takes a series of input tokens and transforms them into a sequence of latent text embeddings.
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  User interface Gradio is anopen-source Python library that simplifies building User Interfaces (UIs) for machine learning (ML) models, APIs, or any Python functions. It allows developers to create interactive interfaces quickly, which can be used to test, showcase, or deploy models. In the provided code, Gradio serves as the UI layer for generating images using the Stable Diffusion model.
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  Advantages of Gradio •Rapid Prototyping: Ideal for quickly building interfaces to test and debug ML models. • Collaboration: Shareable links allow users or stakeholders to interact with the model remotely. • Flexibility: Supports various input/output types (text, image, audio, or video). • Open Source: Freely available and well-documented for easy integration.
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  CONCLUSION The project prioritizesaccessibility and considerations for ethics. It is computationally efficient and user friendly, thus easily accessible to the individual and even to small organizations. Additionally, safeguards against misuse include watermarking and input validation, assuring responsible deployment of AI and avoiding unfairness and other forms of harm. It can generate contextual and diverse outputs while keeping the results realistic; this ability is usually well beyond the capabilities of a generic model. With ethical implications and user- centric design combined, this project stands as a power gap between generic text-to-image systems and personalized content creation. The innovation opens doors to more progress in personalized AI, which empowers users with previously unseen levels of creativity and functionality.
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  THANK YOU